Microseismic event detection and location by continuous map migration

ABSTRACT

The present invention provides methods and systems for microseismic hydraulic fracture monitoring in real-time. The methods and systems of the present invention may include continuous map migration of recorded microseismic signals. The methods and systems provide robust automated simultaneous detection and location of microseismic events.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation application of co-pending U.S. patent applicationSer. No. 10/943,754 filed 17 Sep. 2004 and entitled “Microseismic EventDetection and Location By Continuous Map Migration,” which is herebyincorporated in its entirety by this reference.

FIELD OF THE INVENTION

The present invention relates generally to methods and systems forinvestigating subterranean formations. More particularly, this inventionis directed to methods and systems for detecting and locatingmicroseismic events by continuous map migration.

BACKGROUND OF THE INVENTION

In order to improve the recovery of hydrocarbons from oil and gas wells,the subterranean formations surrounding such wells can be hydraulicallyfractured. Hydraulic fracturing is used to create small cracks insubsurface formations to allow oil or gas to move toward the well.Formations are fractured by introducing specially engineered fluids athigh pressure and high flow rates into the formations through thewellbores. Hydraulic fractures typically extend away from the wellbore250 to 750 feet in two opposing directions according to the naturalstresses within the formation.

The fracture fluids are preferably loaded with proppants, which areusually particles of hard material such as sand. The proppant collectsinside the fracture to permanently “prop” open the new cracks or poresin the formation. The proppant creates a plane of high-permeability sandthrough which production fluids can flow to the wellbore. The fracturingfluids are preferably of high viscosity, and therefore capable ofcarrying effective volumes of proppant material.

Recently, there has been an effort to monitor hydraulic fracturing andproduce maps that illustrate where the fractures occur and the extent ofthe fractures. Current hydraulic fracture monitoring comprises methodsof processing seismic event locations by mapping seismic arrival timesand polarization information into three-dimensional space through theuse of modeled travel times and/or ray paths. Travel time look-up tablesmay be generated by modeling for a given velocity model. A typicalmapping method is commonly known as the “Non-Linear Event Location”method. Non-linear event location has been used historically to locatemacro seismic events such as earthquakes, and is described, for example,at http://geoazur.unice.fr/PERSO/lomax/nlloc/. This and other equivalentmethods are referred to herein as non-linear event location methods.

The non-linear event location methods involve the selection and timepicking of discreet seismic arrivals for each of multiple seismicdetectors and mapping to locate the source of seismic energy. However,to successfully and accurately locate the seismic event, the discretetime picks for each seismic detector need to correspond to the samearrival of either a “P” or “S” wave and be measuring an arrivaloriginating from the same microseismic or seismic event. During afracture operation, many hundreds of microseismic events may begenerated in a short period of time. Current techniques employed in theindustry require considerable human intervention to quality control thetime picking results. It can often take weeks from the time of recordingand detecting the microseismic events to produce accurate maps of theevent locations. Even so, the result, which requires human interactionand interpretation, can lead to multiple and non-reproducible solutions.

Therefore, current methods of real-time monitoring and modeling offracture growth are typically based on pumping and pressure data, whichprovides very limited information concerning the geometry of fracturegrowth. There is a need for microseismic event detection and locationthat can be implemented in real time and enable an operator to adjusthydraulic fracture parameters during the fracture job.

SUMMARY OF THE INVENTION

The present invention meets the above-described needs and others.Specifically, the present invention provides methods and systems forhydraulic fracture microseismic monitoring in real-time. The methods ofthe present invention may include continuous map migration of recordedmicroseismic signals. The methods and systems provide robust automatedsimultaneous detection and location of microseismic events.

The methods and systems of the present invention may be applied to anymicroseismic operation relating to subterranean formations, including,but not limited to hydraulic fracture operations. Application of theprinciples of the present invention provides a method comprisingmonitoring microseismicity. The monitoring comprises receiving seismicsignals with seismic detectors and generating a time evolving historicalmap of microseismic activity by mapping based on the seismic signals.

According to one aspect of the disclosure a method of detecting andlocating a microseismic event is disclosed. The method includesproviding an estimate of space for a location of a source, providing anestimate of a velocity model for a formation, receiving data of amicroseismic event with a first seismic sensor located on a tool in awellbore, receiving data of the microseismic event with a second seismicsensor located on the tool, selecting an instant in time, selecting aplurality of locations in the estimated space, determining whether thedata received by the first sensor and the data received by the secondsensor coalesce at any of the selected locations at the instant, anddetermining the location of the source.

According to another aspect of the disclosure a method of detecting andlocating a microseismic event is disclosed. The method includesproviding an estimate of space for a location of a source of themicroseismic event, the estimate being at least partially based on aestimated relative location of a tool and the microseismic event,providing an estimate of a velocity model for a formation, receivingdata of the microseismic event with a first and a second seismic sensorlocated on the tool in a wellbore, the data including at least a P-waveand an S-wave component, selecting at least a first and a second instantin time, attempting to coalesce the signal received by the first and thesecond sensors at the first instant in time at a plurality of locationsin the space, attempting to coalesce the signal received by the firstand the second sensors at the second instant in time at the plurality oflocations in the space, coalescing the signal received by the first andthe second sensors, and determining the location of the source.

According to another aspect of the disclosure a method of detecting andlocating a microseismic event is disclosed. The method includesproviding an estimate of space for a location of a source of themicroseismic event, providing an estimate of a velocity model for aformation, receiving data of the microseismic event with a first and asecond seismic sensor located on the tool in a wellbore, automaticallyselecting at least a first and a second instant in time, automaticallyattempting to coalesce the signal received by the first and the secondsensors at the first instant in time at a plurality of locations in thespace, automatically attempting to coalesce the signal received by thefirst and the second sensors at the second instant in time at theplurality of locations in the space, automatically coalescing the signalreceived by the first and the second sensors, and automaticallydetermining the location of the source.

Additional advantages and novel features of the invention will be setforth in the description which follows or may be learned by thoseskilled in the art through reading these materials or practicing theinvention. The advantages of the invention may be achieved through themeans recited in the attached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate preferred embodiments of thepresent invention and are a part of the specification. Together with thefollowing description, the drawings demonstrate and explain theprinciples of the present invention.

FIG. 1 is a flowchart illustrating a method or process of microseismicmonitoring according to one aspect of the present invention.

FIG. 2 a partial expansion of the flowchart illustrated in FIG. 1showing an expansion of the continuous arrival detection transformaccording to one aspect of the present invention.

FIG. 3 is an illustration of sample waveforms and an application ofmonitoring functions to the waveforms according to some aspects of thepresent invention.

FIG. 4 is an expansion of the flowchart illustrated in FIG. 2 to includethe use of hash tables according to one embodiment of the presentinvention.

FIG. 5 is an expansion of the flowcharts illustrated in FIGS. 1 and 4 toinclude a spatial maximum coherence and location function according toone embodiment of the present invention.

FIG. 6 is a trivial 1D time evolution map of a seismic signal receivedby a single seismic detector according to one aspect of the presentinvention.

FIG. 7A is an example of a time evolution map prior to maximum spatialcoalescence using a single seismic detector according to one aspect ofthe present invention.

FIG. 7B is the time evolution map of FIG. 7A at maximum spatialcoalescence.

FIG. 7C is the time evolution map of FIG. 7A after maximum spatialcoalescence.

FIG. 8A is an example of a time evolution map prior to maximum spatialcoalescence using a multiple seismic detectors according to one aspectof the present invention.

FIG. 8B is the time evolution map of FIG. 8A at maximum spatialcoalescence.

FIG. 8C is the time evolution map of FIG. 8A after maximum spatialcoalescence.

FIG. 9A is snapshot of a time evolution map mapping a seismic signalmeasured by three sensor locations before maximum coalescence accordingone aspect of the present invention.

FIG. 9B is a snapshot of the time evolution map of FIG. 9A at maximumcoalescence.

FIG. 10A is an example of a reverse time evolution map prior to maximumspatial coalescence using a multiple seismic detectors according to oneaspect of the present invention.

FIG. 10B is the reverse time evolution map of FIG. 10A at maximumspatial coalescence.

FIG. 10C is the reverse time evolution map of FIG. 10A after maximumspatial coalescence.

FIG. 11A an example of a cross-sectional snapshot of a 3D spatial mapshowing the spatial coalescence just prior to a maximum of a combinedmapped event detection criteria and waveform polarization analysis foran eight level array of three-component sensors according to oneembodiment of the present invention.

FIG. 11B is a snapshot according to FIG. 11A at maximum coalescence.

FIG. 11C is a snapshot according to FIG. 11A after maximum coalescence.

FIG. 12A illustrates a process of computing a continuous map ofcoalescence according to one aspect of the present invention.

FIG. 12B illustrates a process of computing an evolving map ofcoalescence over short time windows of interest.

FIG. 13 is an example of a plot of the time evolution of the spatialmaximum coherency according to one embodiment of the present invention.

FIG. 14 illustrates a seismic monitoring system and a hydraulicfracturing system according to one embodiment of the present invention.

Throughout the drawings, identical reference symbols designate similar,but not necessarily identical, elements.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Illustrative embodiments and aspects of the invention are describedbelow. It will of course be appreciated that in the development of anysuch actual embodiment, numerous implementation-specific decisions mustbe made to achieve the developers' specific goals, such as compliancewith system-related and business-related constraints that will vary fromone implementation to another. Moreover, it will be appreciated thatsuch a development effort might be complex and time-consuming, but wouldnevertheless be a routine undertaking for those of ordinary skill in theart having the benefit of this disclosure.

As used throughout the specification and claims, the terms “automatic”or “automated” means without human intervention or interpretation.“Coalesce” or “coalescence” means coming together of different measuresinto a map such as a spatial map. Coalescence also means evaluating thetime evolving contributions from multiple sensors or components, butdoes not include non-linear event location methods. The terms “coalesce”or “coalescence” may be further understood in reference to the FIGS.7A-10C below. A “sensor” defines a single device location that may haveone or more “detectors” capable of receiving measurements at the samelocation. The words “including” and “having,” as used in thespecification, including the claims, have the same meaning as the word“comprising.”

The present invention contemplates real-time monitoring and modeling offracture growth. The methods and systems provided herein facilitatetransforming microseismic signals as recorded by one or more seismicdetectors and transforming the signal to enhance detection of arrivals.Thereafter, the methods and systems map or migrate the transformationresult using a set of travel time and ray direction look-up tables. Thismap or migration generates a continuously updated two-dimensional orthree-dimensional historical spatial map, such that a snapshot of themap represents a measure of the likelihood that there was a source ofseismic energy occurring at that time, at each point in space.

According to aspects of the present invention, the time evolution of themap is captured, and by evaluating maximum values of the map that exceeda detection threshold, a determination is made whether a source ofseismic energy or an event occurred at a given time and location. Bychoosing a function that coalesces or correlates the contributions fromeach detector, seismic sources characterized by P-wave, S-wave, orsources generating both P and S-waves, can be distinguished. The wavetypes may be distinguished, for example, by considering that P and Swaves have different travel times and different waveform polarization.

According to some aspects of the invention, detecting a time anddetermining a unique location for a seismic event is dependent on thenumber of seismic detectors used, the relative positions of thedetectors, and a choice of functions used to coalesce or correlate thecontributions of the detectors. The resulting map at the time of theevent captures the uniqueness of the determined location.

The mapping to generate an evolving historical map of the sources ofseismic energy according to principles of the present invention offersmany advantages over traditional methods of detecting and time pickingarrivals for individual seismic detectors. Some of the advantages arediscussed below. For example, the methods and systems of the presentinvention may be fully automated and able to operate continuously intime for monitoring, detecting, and locating microseismic events.Methods of the present invention utilize the contributions from one ormultiple seismic detectors. By coalescing or correlating thecontributions from multiple seismic detectors, events may be detectedand located that could not otherwise be detected, time picked, andlocated by traditional methods that analyze only the signal ofindividual seismic detectors. Methods of the present invention may beadapted by changing a coalescence function to detect eventscharacterized by difference source parameters. The principles of thepresent invention capture the uniqueness of an event location ascharacterized by the seismic measurement, the seismic detector geometry,and the choice of coalescence function. Accordingly, methods of thepresent invention reduce the solution for complex geometry, geology, andgeophysical models to the generation and use of forward modeled traveltimes and ray paths that are generated by any of the appropriate andwell studied methods of geophysical modeling. The principles of thepresent invention also eliminate the need for interactive and manualdiscreet arrival detection and accurate time picking characterized byprior methods. The process of selecting and time picking equivalentarrivals for a number of seismic detectors can be difficult to automate,and often requires a time consuming interactive and interpretiveprocess.

Turning now to the Figures, and in particular to FIG. 1, a method orprocess of monitoring microseismic events according to one embodiment ofthe present invention is illustrated by flowchart. According to theprocess illustrated in FIG. 1, the process can be performed oncontinuous data or on a semi-continuous basis using short time windowsof data, which is represented by a box 102. The method contemplates asequence of transforms which may include continuous arrival detectiontransforms 104 and/or continuous computation of three-component (3C)polarization 106. Following the transforms, mapping or migration 108takes place. The mapping process outputs a continuous value representinga maximum coalescence from a spatial map, along with a location of themaximum coalescence 110. The time and location of maximum coalescenceabove a detection threshold corresponds to a measured time and locationthat an event is reported to have occurred as indicated at 112.

According to some aspects of the present invention, the continuousarrival detection transform 104 discounts a signature of the recordedseismic signal and enhances arrival detection. Therefore, the transformmay involve cross correlation and deconvolution (filtering) to enhanceor measure a signal with a particular wave shape. The transform may alsoinclude arrival detection algorithms or any simple measure of an arrivalsuch as waveform energy or unsigned waveform amplitude. If only anaccurate measure of location of an event is required, the detectiontransform may be any measure of the first arrival signal, as long as themeasurement provides the same response for each of the seismicdetectors. Obtaining accurate timing of the event requires knowledge ofhow the detection transform relates to the timing of the first arrival,but is otherwise unnecessary according to this method.

One exemplary transform that may be used is a variation of the STA/LTA(Short Term Averaging to Long Term Averaging) detection criteria,utilizing a Hilbert envelope as described in U.S. Pat. No. 6,748,330,which is hereby incorporated in its entirety by this reference. TheSTA/LTA method and variations of the method are well known to those ofskill in the art having the benefit of this disclosure and are commonlyused to enhance an arrival for automatic arrival detection and timepicking methods. However, any method, including methods that make use ofmulti-component rectilinearity or enhance or detect arrivals byconsidering changes in signal spectra may also be used. See, forexample, Moriya, H. et. el., 1996.

Nevertheless, unlike conventional methods, according to principles ofthe present invention the detection criteria is not directly used todetect and time pick or identify any particular arrival signal. Rather,the result is continuously mapped into 2D or 3D space using modeledtravel times. The detection is then made on the changing 2D or 3D map,which is the result of the contributions from one or multiple seismicdetectors located on one or more sensors.

According to some embodiments wherein there is a sufficient number ofsuitably located seismic detectors, a unique location of themicroseismic energy can be determined whether the source is identified,modeled, and mapped as strictly a P-wave seismic source, strictly anS-wave seismic source, or a multi-phase seismic source. Uniquelydetermining a 3D location using the arrival times of a single phase (Por S) requires a minimum measure of four arrival times. This is theequivalent phenomenon found in GPS positioning. The four measurementsdetermine the four unknowns: time and the three spatial coordinates. Thefour sensors need to be suitably located in space (i.e. they cannot alllie in one plane). However, making an assumption about the location ofthe source (e.g. fixing that the source was generated on a 2D plane) orfixing the time of the source by another measurement (e.g.electromagnetic timing of an event) the number of seismic detectorsrequired to determine a unique location may be reduced to three.

In some cases, the seismic source is known to generate both P and Sseismic energy. This information may be used to further constrain themapping and determination of source location. Therefore, a smallernumber of more simply located seismic detectors may be sufficient todetermine a unique location of the source of microseismic energy and mapa hydraulic fracture. A single 3C (three-component) or 4C(four-component) detector capable of measuring P-wave polarization(direction of the event) and the P and S arrival time (distance of theevent) may determine the location of the event. Using one 3C detector,two possible solutions may be found without making further assumptionsabout the general direction of the source. Using a 4C detector or addingone or more seismic detectors, a single unique location may bedetermined. These principles will be well understood by those of skillin the art of locating microseismic events having the benefit of thisdisclosure.

According to one exemplary arrangement illustrated by the flowchart ofFIG. 2, a continuous moving window STA/LTA detection transform isapplied to a measured seismic signal for each of a vertical array ofeight, 3C seismic detectors such that the transform is sensitive toeither a P arrival 104A or an S arrival 104B. The transform 104A/104B issensitized to either P or S arrivals by taking an expected P waveformprojection and the expected Sh projection for each of the 3C seismicdetectors, assuming the source location is in a given general direction.Accordingly, the detectability of seismic events is enhanced.

According to another example implementing principles of the presentinvention, the polarization 106 of the 3C detected seismic signal may becomputed on a continuous basis by using a moving window covarianceanalysis and eigenvector decomposition. The waveform polarization 106can be used for the mapping in addition to the arrival detectioncriteria. By comparing a forward modeled P wave arrival and the computed3C polarization of the seismic waveform at each location on the 3D map,the contribution or coalescence can be weighted by the match in modeledray-direction and waveform 3C polarization. For example, a weightingfunction given by the P polarization and model match determines theazimuth of the event location relative to the vertical detector arraydescribed above. For a vertical array of seismic sensors S (each ofwhich has one or more detectors) such as the ones shown in FIG. 14, aunique location for the event is not otherwise determinable.

As shown in FIG. 1, at each time step, T=T0, a 3D spatial mapping ormigration is made for the detection criteria and optionally the 3CP-wave polarization. The mapping is preferably made via a set offorward-modeled travel time and ray-traced look-up tables. The timeevolution of the mapping may utilize a time constant, Tc, and operaterecursively to allow contributions from multiple sensors to sum up inspace and over time.

At a given time T₀, the contribution to the spatial location X_(i),Y_(j), Z_(k) is given as the sum of the contributions of the detectioncriteria from each of a number of multi-component seismic detectors.According to some aspects, the product of the P and S detection criteriais used, weighted by the match of modeled and measured P wavepolarization. The time step evolution and contribution for one seismicdetector is then given as:

$\begin{matrix}{{{Map}\left( {i,j,k} \right)} - {\left( {1 - \frac{1}{T_{c}}} \right)*{{Map}\left( {i,j,k} \right)}} + {\frac{1}{T_{c}}*{P\_ SNR}*{S\_ SNR}*{P\_ Match}}} & (1)\end{matrix}$

Where:

-   -   P_SNR=P Detection SNR at time T₀+T_(p)(i, j, k);    -   S_SNR=S Detection SNR at time T₀+T_(s)(i, j, k); and    -   P_Match=dot (Modeled_P_Vector (i, j, k), Waveform Vector        (T₀+T_(p)(i, j, k))²        For multiple seismic detectors, the P_SNR*S_SNR*P_Match is        summed, thereby summing the spatial contribution of each        receiver at each location at each time step.

The recursive equation (1) is a computationally efficient method of amore general method using the sum of time windowed weightedcontributions, using a time windowing function such as a hanning window.The purpose of applying a time windowing function, or a recursive timeconstant “Tc” as described above, is so that the contributions of themultiple measures from the one or more sensors do no miss each other inspace, due to inaccuracies in the modeled travel times used in themapping.

The contribution function defined by equation (1) used as a measure ofthe coalescence of P and S arrivals for the array of multi-componentseismic detectors is not unique. Any function that takes into accountthe contribution as the sum, product, or power of the contribution ofthe P, S, or both the P and S detection criteria, as well as optionallya measure of the match between the forward modeled and measured 3Cpolarization, may be used.

The choice of a contribution or correlation function depends on thedesired sensitivity to P only, S only, or P+S only seismic sources.According to one exemplary embodiment, by using and detecting onlyseismic sources which have both a P and an S arrival, the location asmeasured by the single vertical array of seismic detectors is betterconstrained.

The process described above is illustrated in FIG. 3, which shows anexemplary P-wave detection transform 304A, an S-wave detection transform304B, and a continuous 3C polarization vector or angles of waveformpolarization 306. Continual 3D spatial mapping 308 follows and isupdated at each time step T=T0. Event detection criteria 312 is themaximum of the 3D spatial coalescence at every time step. A seismicevent is triggered when a detection threshold is exceeded. The time ofmaximum coalescence, time T=Te, is the time the event occurred. The X,Y, and Z locations of the maximum 3D spatial coalescence for each time Tis stored. The detected event location Xe, Ye, Ze is given at time T=Teas illustrated in table 322.

The principles of the present invention are not limited to the specificmethods described above. Variations of the methods described above mayalso be implemented, for example, to improve computation performance.One example of such a variation may be the use of hash tables to storepre-computed values prior to mapping as shown in FIG. 4. Using hashtables may reduce the 3D spatial mapping or migration to a simple sum ofcontributions from each of the seismic detectors. The use of hash tablesis well known to those of skill in the art having the benefit of thisdisclosure for computational code optimization and other uses.

Another variation incorporating methods of the present invention isillustrated by the flowchart of FIG. 5. If the seismic detectors of amicroseismic monitoring system exist as a single vertical array ofsensors, using cylindrical coordinates, a method can be implemented toefficiently map and determine event location using only 2D mapping. The2D mapping is done as a function of R, which is defined as the distancefrom the array, and position Z. If an event detection occurs at time Te,which is the time of maximum coalescence of the 2D mapping, the mappingalso determines the distance R and the position Z of the event. Theazimuth angle of the event relative to the array can then be determinedfrom the polarization analysis for the arriving P wave.

The methods described above have been implemented to monitormicroseismic events as described below. A signal recorded by a verticalarray of eight three-component seismic sensors was analyzed. The 3Cwaveforms were projected to a nominal P polarization and Sh polarizationbased on the geometry, sensor orientation, and anticipated arrivaldirection of microseismic energy. The nominal P and Sh waveforms weredetection transformed using a Hilbert envelope STA/LTA detectioncriteria. Using a ray-traced modeled travel time and ray directionlookup tables, the recorded data was migrated to update a 3D volume(spatial map) on a continuous basis using the methods described above.

The general form of the map can be considered 1D, 2D, or 3D. A 2D mapmay be constructed from multiple 1D maps, and a 3D map may beconstructed from multiple 2D maps. The spatial coordinates of the mapmay be position, distance, or location in one, two, or three dimensions.

In the case of a homogeneous spatial velocity model, the translation ofa “time” coordinate to a spatial or distance coordinate is a simplescalar (velocity). Therefore, in such a case the “time” coordinate isconsidered equivalent to a “spatial coordinate.”

As the map is time evolving, it is difficult to show or display theresult of the map with static figures. The exception is a 1D map. The 1Dmap can be shown graphically as a 2D image of the time evolution. Todisplay a 2D map requires plotting a changing surface, and a 3D maprequires a changing volume plot. Therefore, the evolving map isdescribed below with multiple static snapshots. A 1D map of the resultof the signal for a single seismic detector may be a trivial map. Forcompleteness, a snapshot of the trivial 1D map for a single seismicdetector is shown in FIG. 6. The 1D map is a plot of a time evolvingmeasure of a seismic signal, where the measure of the signal is arepresentation of the measure of interest. However, this is a trivialmap and of minor interest.

Any multiple measure of the signal of interest (from one or more seismicdetectors) may be mapped as a map of coalescence. According to thepresent invention, a map of coalescence is historical and time evolving.In the simple 1D example of a signal as recorded by a single seismicdetector, the signal may be interpreted as a measure of both a P-waveand an S-wave. The map of coalescence is then generated based on thesemeasures.

P and S-waves travel at different speeds Therefore, the spatialcoordinate is either measured in distance or measured in time referringto either P velocity or S velocity. According to the example shown inFIG. 7A-7C, the signal is mapped twice using a single detector, onceusing the P velocity 730, and once using the S velocity 732. The signalmay contain P-waves 734 and S-waves 736 at both velocities. FIGS. 7A-7Cillustrate three time snapshots of the evolving map. FIG. 7A illustratesa snapshot in time prior to coalescence. FIG. 7B illustrates the time ofcoalescence wherein the measured waves coalesce at a point 740. FIG. 7Cshows the map after coalescence. As this is a process of “putting” thesignal back to its spatial origin (time or distance), this might bedescribed as a process of migration, as the term “map migration” hasbeen used herein. The time evolution of the map of FIGS. 7A-7C shows theP-wave 730 and the S-wave 732 traveling at different speeds. The netresult or map of coalescence is the time evolving resultant contributionof the two waveforms. The contribution of the P-wave 730 and the S-wave732 may be a simple sum or product of the two signals, or some functionthe makes a quantitative measure of the contribution of the two signals.

When the one or more seismic sensors comprise a 3C or othermulti-component detector, seismic P-wave signals 830 and S-wave signals832 shown in FIGS. 8A-8C also have a measure of waveform polarization.P-waves and S-waves have different polarization. Therefore, a functionof coalescence that takes into account the waveform polarization may beconstructed. Such a function would only exhibit coalescence when alikely P and S-wave coincide (as shown in FIG. 8B), and not when two Pwaves or two S waves coincide. At a point of coincidence 840, anoccurrence is P+S energy occurs at a correct distance and time.Accordingly, incidental coalescence of two P-waves or two S-waves thatmay result, for example, from two independent seismic events, can beexcluded.

In addition to or alternative to P-wave and S-wave coalescence, themethods of the present invention also contemplate spatial coalescence ofsignals for multiple seismic sensors at different locations. Measuring aunique location in 2D for a single phase (either a P signal or an Ssignal) requires a minimum of three seismic sensors. For a uniquelocation in 3D, a minimum of four sensors may be required. The timeevolution of the map is the measure of coalescence which is the measureof interest.

The 2D (or 3D) map can be generated by interpreting and mapping thesignals as P only, S only, or P+S. The map can be generated by directlymapping the signals, or the signals may be efficiently mapped making useof 1D time-indexed maps generated for each of the single or multicomponent sensors. Time indexed 1D maps are termed “hash tables.” Thehash tables may pre-compute a measure of the P+S coalescence for eachdetector and thereby simplify the computation of the 2D or 3D spatialcoalescence map. FIGS. 9A-9B illustrate spatial coalescence mapping withthree sensor locations, S1, S2, and S3. FIG. 9A illustrates a timesnapshot of traveling waveform signals before coalescence, and FIG. 9Billustrates a plot at time=Te, the time of coalescence, wherein thewaveforms come together at a point 940.

A 2D or 3D map may be generated, which may be considered to be multiple1D maps. Additionally, the 2D and 3D maps may sum the contributions frommultiple seismic detectors and form a 2D or 3D map of coalescence. Inthe case of multiple seismic detectors spatially separated at differentsensor locations such as the case illustrated in FIGS. 9A-9B, the timeevolving 2D or 3D map of coalescence of any measure of the signal may beof interest. The mapping of a function of coalescence of measures of aseismic signal, as they relate to P-wave only, S-wave only, or both Pand S-wave modes, results in a map showing the coalescence of thecontributions from the multiple detectors. The time evolution of thecoalescence map yields the microseismic events of interest.

The description above related to coalescence mapping, forward mapping,or “delayed” forward mapping of coalescence relates to the timeevolution of the map in a forward direction (time increasing). The“delay” relates to generating a real-time map (as data is recorded) at atime delay equivalent to the time of travel from the furthermostdistance covered by the map

However, a reverse map of coalescence may also be generated. With areverse map of coalescence, time is run in reverse. The resultant mapmay be mathematically equivalent to the forward map (but played inreverse), or it may be mathematically different. According to theexamples above, a recursive function involving a time constant Tc isused. A recursive function implies that the next time step depends on apreviously computed result. Thus, the resultant set of forward andreverse maps would not be equivalent.

A reverse generated map implies that it is not available in real-time(i.e., it can only be generated after the data has been acquired).Nevertheless, real-time may be available if the map is “played back” orcomputed over short time windows of data of interest, on a continuousforward time basis. FIGS. 10A-10C illustrate a reverse time evolution ofthe map of coalescence of P and S-waves 1030, 1032.

The continuous maps disclosed herein may be displayed for operators inany number of ways. The displays may be different depending on thenumber of map dimensions. Examples of one, two, and three-dimensionalmapping display techniques are described below.

Display of a 1D may be a time evolving map as described above.Additionally, it is possible to capture the time evolution of the map asa 2D image. For example, the horizontal axis may be defined as a 1Dspatial coordinate, and the vertical axis may be defined as the timeevolution of the map. Accordingly, the value or measure of coalescencemay be represented as a color, density, or contour, or 2D surface havingheight.

A 2D map or display of a surface may only be easily displayed as ananimation, or as a sequence of snapshots. Two axes of the surfacerepresent the spatial coordinates, with the surface value (measure ofcoalescence) displayed, for example, as a color, density, contour, or asa 2D surface having height. The value of the surface changes with time.The time evolution of the maximum values of coalescence may be capturedand displayed in a simple plot. FIGS. 11A-11C illustrate one exemplarymap of coalescence 1150 generated by mapping processed signals 1152measured, for example, by an array of eight 3C seismic sensors. FIGS.11A-11C illustrate, respectively, a snapshot just before coalescence, atcoalescence, and after coalescence.

One way to display a 3D map is a time evolving iso-surface. For example,the iso-surface enclosing a volume representing the highest ten percentmeasure of coalescence may be shown. The value of coalescence may bedisplayed as a color or by opacity. The time evolution of the 3D spatialmap is a visible representation of the 3D spatial measure ofcoalescence, and may be automatically interpreted as the 3D spatialmeasure of microseismicity.

The microseismicity may be defined as either a P-wave seismic source, anS-wave seismic source, or a measure of P and S only seismic sources.Where the measure of coalescence can be quantified, the resultant mapmay be used quantitatively as a measure of the probability of a sourceof seismic energy as revealed by the experiment, being the measurement,mapping and interpretation.

Maps of coalescence are displayed here left-to-right as the naturaldirection of increasing forward time. FIG. 12A depicts a process ofcomputing a continuous delayed forward map of coalescence. At the leftof FIG. 12A, there will be a starting point representing a start torecording data. At the right will be an end point, at which timerecording ceases. Time “T” therefore increases from left to right.Alternatively, an equivalent reverse time coalescence map may becomputed following the recording of all data of interest. Moreover,according to some embodiments a map of coalescence relating to recordeddata may be generated over short time-windows of interest. For example,as shown in FIG. 12B, coalescence maps may be generated in a firstwindow defined between times T1 and T2, and second window definedbetween times T3 and T4. The time evolving map of coalescence may becomputed over short or long windows either forward or backward.

The maximum coherency or coalescence value at each time step and itslocation are recorded as discussed above as a 1D, 2D, or 3D map ofcoalescence. For the map of coalescence, events are identified as shownin FIG. 13. FIG. 13 is an exemplary plot of the time evolution of thespatial maximum coherency. Three microseismic events are identified asTe1, Te2, and Te3 in FIG. 13. The times Te1 correspond to the mostlikely times of the events as determined from the analysis (mapping) ofthe recorded data.

It will be understood by those of skill in the art having the benefit ofthis disclosure that the descriptions herein for generating a map ofcoalescence from raw recorded signals are exemplary. Numerous methods ofsignal analysis prior to mapping may also be applied, includes processesof detection transformation according to principles of the presentinvention. Measures of the signal may be related to measures of energy(amplitude), rectilinearity, or changes in signal frequency content.Moreover, there may be measures of P and S-wave signals in addition towaveform polarization, including analysis of frequency content.

The methods and systems described above may be implemented in real time,for example, by a hydraulic fracture and monitoring system 1460 shown inFIG. 14. The hydraulic fracture and monitoring system 1460 system ispreferably arranged with respect to a first and a second wellbore 1462,1464. The first wellbore 1462 traverses a formation 1466 with a zone1468 that is scheduled for hydraulic fracture. A hydraulic fractureapparatus 1470 comprising a fracture fluid, a pump, and controls iscoupled to the first wellbore 1462. The second wellbore 1464 contains aone or more, and preferably a plurality, of temporary or permanentseismic sensors S. Alternatively, the sensors S may be placed along asurface 1472 or within the first wellbore 1462. A communication cablesuch a telemetry wire 1474 facilitates communication between the sensorsS and a computer data acquisition and control system 1476. As a fracturejob commences, fracture fluid is pumped into the first wellbore 1462,creating microseismic events 1478 as the zone 1468 cracks andpropagates. The microseismic events 1478 create seismic waves that arereceived by detectors of the sensors S.

The seismic signals received by the sensors S may be used to monitor andmap microseismic events caused by the fracture operation. Accordingly,based on the seismic signals received, computers, such as the computerdata acquisition and control system 1476, may run programs containinginstructions, that, when executed, perform methods according to theprinciples described herein. Furthermore, the methods described hereinmay be fully automated and able to operate continuously in time formonitoring, detecting, and locating microseismic events. An operator1479 may receive results of the methods described above in real time asthey are displayed on a monitor 1480. The operator 1479 may, in turn,adjust hydraulic fracture parameters such as pumping pressure,stimulation fluid, and proppant concentrations to optimize wellborestimulation based on the displayed information relating to detected andlocated seismic events.

The preferred aspects and embodiments were chosen and described in orderto best explain the principles of the invention and its practicalapplication. The preceding description is intended to enable othersskilled in the art to best utilize the invention in various aspects andembodiments and with various modifications as are suited to theparticular use contemplated. The description may be implemented in anymicroseismic measurement system, particularly for hydraulic fracturemonitoring. In addition, the methods may be programmed and saved as aset of instructions, that, when executed, perform the methods describedherein. It is intended that the scope of the invention be defined by thefollowing claims.

1. A method of detecting and locating a microseismic event, comprising:providing an estimate of space for a location of a source; providing anestimate of a velocity model for a formation; after providing theestimates, receiving data of a microseismic event with a first seismicsensor located on a tool in a wellbore; receiving data of themicroseismic event with a second seismic sensor located on the tool;selecting an instant in time; selecting a plurality of locations in theestimated space, wherein the estimated space is not between the firstand second sensors; utilizing the receiving data to determine a firstbreak using forward modelled traveltimes; determining, with a processor,whether the data received by the first sensor and the data received bythe second sensor coalesce at any of the selected locations at theinstant; and determining, with a processor, the location of the source.2. A method according to claim 1, wherein the estimate of space is atleast partially based on a relative location to a tool.
 3. A methodaccording to claim 2, wherein the estimate of space is at leastpartially based on a relative location of the tool to a known area in asubterranean formation undergoing hydraulic fracturing.
 4. A methodaccording to claim 3, wherein the hydraulic fracturing is occurring in asecond wellbore and the receiving is occurring in a first wellbore.
 5. Amethod according to claim 4, wherein the receiving is at least partiallyoccurring above the hydraulic fracturing.
 6. A method according to claim1, wherein the first and second seismic sensors each receive at leastone of a P-wave and an S-wave component resulting from the microseismicevent.
 7. A method according to claim 6, wherein the first and secondseismic sensors each receive both the P-wave and the S-wave component.8. A method according to claim 1, wherein the microseismic event occursduring a period of time recorded by both the first and second seismicsensors.
 9. A method according to claim 1, wherein the first and thesecond seismic sensors are located on the tool in the same plane.
 10. Amethod according to claim 8, wherein the selected instant in time is thefirst instant of the period of time.
 11. A method according to claim 1,wherein determining the location of the source includes at least one ofdetermining a 2-D location and a 3-D location of the source.
 12. Amethod according to claim 1, further including selecting a plurality ofinstances in time and selecting a plurality of locations in space foreach of the instances in time.
 13. A method according to claim 12,wherein determining the location of the source is based on the pluralityof instances and the plurality of locations.
 14. A method according toclaim 12, further including generating a time evolving map ofmicroseismic activity by spatial mapping the seismic data.
 15. A methodaccording to claim 1, further comprising reporting the microseismicevents to a hydraulic fracture operator in real-time.
 16. A methodaccording to claim 1, wherein at least one of selecting an instant intime, selecting a plurality of locations in the estimated space,determining whether the data received by the first sensor and the datareceived by the second sensor coalesce at any of the selected locationsat the instant, and determining the location of the source are fullyautomated.
 17. A method according to claim 1, wherein selecting aninstant in time, selecting a plurality of locations in the estimatedspace, determining whether the data received by the first sensor and thedata received by the second sensor coalesce at any of the selectedlocations at the instant, and determining the location of the source areall fully automated.
 18. A method of detecting and locating amicroseismic event, comprising: providing an estimate of space for alocation of a source of the microseismic event, the estimate being atleast partially based on a estimated relative location of a tool and themicroseismic event; providing an estimate of a velocity model for aformation; receiving data of the microseismic event with a first and asecond seismic sensor located on the tool in a wellbore, the dataincluding at least a P-wave and an S-wave component; selecting at leasta first and a second instant in time; utilizing the receiving data todetermine a first break using forward modelled traveltimes; attempting,with a processor, to coalesce the signal received by the first andsecond sensors at the first instant in time at a plurality of locationsin the space; attempting, with a processor, to coalesce the signalreceived by the first and second sensors at the second instant in timeat a plurality of locations in the space; coalescing the signal receivedby the first and the second sensors; and determining the location of thesource.
 19. A method according to claim 18, wherein selecting at least afirst and a second instant in time, attempting to coalesce the signals,coalescing the signal received by the first and the second sensors, anddetermining the location of the source are all fully automated.
 20. Amethod according to claim 18, wherein the estimate of space is at leastpartially based on a relative location of the tool to a known area in asubterranean formation undergoing hydraulic fracturing.
 21. A methodaccording to claim 20, wherein the hydraulic fracturing is occurring ina second wellbore and the receiving is occurring in a first wellbore.22. A method of detecting and locating a microseismic event, comprising:providing an estimate of space for a location of a source of themicroseismic event; providing an estimate of a velocity model for aformation; after providing the estimates, receiving data of themicroseismic event with a first and a second seismic sensor located on atool in a wellbore; automatically selecting at least a first and asecond instant in time; automatically utilizing the receiving data todetermine a first break using forward modelled traveltimes;automatically attempting, with a processor, to coalesce the signalreceived by the first and the second sensors at the first instant intime at a plurality of locations in the space; automatically attempting,with a processor, to coalesce the signal received by the first and thesecond sensors at the second instant in time at the plurality oflocations in the space; automatically coalescing the signal received bythe first and the second sensors; and automatically determining thelocation of the source, wherein the location is not between the firstand second sensors.